﻿ 集装箱海运网络脆弱性风险控制
 舰船科学技术  2023, Vol. 45 Issue (10): 152-155    DOI: 10.3404/j.issn.1672-7649.2023.10.030 PDF

Vulnerability risk control of container shipping network
ZHANG Xiao-ling
Henan Wisdom Education and Intelligent Technology Application Engineering Technology Research Center, Zhengzhou 451460, China
Abstract: To ensure the stable operation of the container shipping network, a vulnerability risk control method for the container shipping network is proposed. The average degree of container shipping network, the proportion of isolated nodes in container shipping network, and the efficiency of container shipping network are selected as the vulnerability risk control indicators. The entropy weight method is used to calculate the weight of each indicator. Based on the weight of the indicators, a vulnerability risk classification model is constructed. The vulnerability risk of container shipping network is divided into five levels, namely, non fragile to very fragile. According to the classification results, Put forward corresponding strategies and suggestions to realize vulnerability risk control of container shipping network. The experimental results show that this method can effectively obtain the results of vulnerability risk classification, and effectively reduce the vulnerability risk of container shipping network.
Key words: lritime transport network     lerability     lk control     Risk control indicators     entropy weight method     risk level
0 引　言

1 集装箱海运网络脆弱性风险控制方法 1.1 集装箱海运网络脆弱性风险控制指标

1)集装箱海运网络平均度

 $P = \frac{{\displaystyle\sum\limits_{i = 1}^S {{p_i}} }}{S} 。$ (1)

2)集装箱海运网络孤立节点比例

 $\Delta S = \left( {1 - \frac{{{S^ * }}}{S}} \right) \times 100\% 。$ (2)

3)集装箱海运网络聚集系数

 $\partial = \frac{{\displaystyle\sum\limits_{i = 1}^S {\frac{{2{M_i}}}{{{p_i}\left( {{p_i} - 1} \right)}}} }}{S},i = 1,2,3, \cdots ,S 。$ (3)

4)平均路径长度

 $L = \frac{{2\displaystyle\sum\limits_{i = 1}^S {\sum\limits_{j = i + 1}^S {{d_{ij}}} } }}{{S\left( {S - 1} \right)}} 。$ (4)

5)集装箱海运网络效率

 $\phi = \frac{{\displaystyle\sum\limits_{i = 1}^S {\sum\limits_{j = 1\left( {j \ne 1} \right)}^S {{h_{ij}}} } }}{{S\left( {S - 1} \right)}} 。$ (5)

6)最大连通子图相对大小

 $T = \frac{{S'}}{S} 。$ (6)

7)集装箱海运网络节点强度

 ${\delta _i} = \sum\limits_{j = 1}^S {{q_{ij}}} 。$ (7)

1.2 基于熵权法的指标权重计算

1)构建m个集装箱海运网络n个集装箱海运网络脆弱性风险控制指标的判断矩阵：

 $R = \left( {{x_{ij}}} \right)i = 1,2, \cdots ,n;j = 1,2, \cdots ,m 。$ (8)

2)归一化处理 $R$ ，获取归一化判断矩阵：

 ${\boldsymbol{B}} = \left( {{b_{ij}}} \right) = \frac{{{x_{ij}} - {x_{\min }}}}{{{x_{\max }} - {x_{\min }}}} ，$ (9)

3)依照熵的概念，基于m个集装箱海运网络n个集装箱海运网络脆弱性风险控制指标能够确定指标熵为：

 $\left\{ \begin{gathered} {A_i} = - \frac{1}{{\ln m}}\left[ {\sum\limits_{j = 1}^m {{f_{ij}}\ln {f_{ij}}} } \right] ，\\ {f_{ij}} = {b_{ij}}/\sum\limits_{j = 1}^m {{b_{ij}}} 。\\ \end{gathered} \right.$ (10)

4)确定集装箱海运网络脆弱性风险控制指标的熵权：

 $\left\{ \begin{gathered} W{\text{ = }}{\left( {{w_i}} \right)_{1 \times n}}，\\ {w_i} = (1 - {A_i})/n - \sum\limits_{i = 1}^n {{A_i}} \;\;。\\ \end{gathered} \right.$ (11)
1.3 脆弱性风险划分模型

1)构建因素集

 ${x_i} = \left\{ {{x_{i1}},{x_{i2}}, \cdots ,{x_{im}}} \right\} 。$ (12)

2)脆弱性风险等级划分

3)构建模糊划分矩阵

4)模糊综合划分

 ${\boldsymbol{D}}{\text{ = W}} {R_1} 。$ (13)

2 性能测试与结果分析

 图 1 研究对象结构概况 Fig. 1 Overview of the structure of the research object
2.1 指标权重计算

2.2 脆弱性风险等级划分结果

2.3 风险控制效果分析

 图 2 研究对象的脆弱性风险等级变化情况 Fig. 2 Change of vulnerability risk level of research objects
3 结　语

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